## 5.11 Seasonal ARIMA model

The Chinook data are monthly and start in January 1990. To make this into a ts object do

chinookts <- ts(chinook\$log.metric.tons, start = c(1990, 1),
frequency = 12)

start is the year and month and frequency is the number of months in the year.

Use ?ts to see more examples of how to set up ts objects.

### 5.11.1 Plot seasonal data

plot(chinookts)

### 5.11.2auto.arima() for seasonal ts

auto.arima() will recognize that our data has season and fit a seasonal ARIMA model to our data by default. Let’s define the training data up to 1998 and use 1999 as the test data.

traindat <- window(chinookts, c(1990, 10), c(1998, 12))
testdat <- window(chinookts, c(1999, 1), c(1999, 12))
fit <- forecast::auto.arima(traindat)
fit
Series: traindat
ARIMA(1,0,0)(0,1,0)[12] with drift

Coefficients:
ar1    drift
0.3676  -0.0320
s.e.  0.1335   0.0127

sigma^2 estimated as 0.8053:  log likelihood=-107.37
AIC=220.73   AICc=221.02   BIC=228.13

Use ?window to understand how subsetting a ts object works.